Overview

Dataset statistics

Number of variables18
Number of observations1274
Missing cells2330
Missing cells (%)10.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory179.3 KiB
Average record size in memory144.1 B

Variable types

Numeric12
Categorical6

Alerts

City_Mileage has constant value "12.7" Constant
Ex-Showroom_Price is highly correlated with Displacement and 5 other fieldsHigh correlation
Displacement is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Cylinders is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Fuel_Tank_Capacity is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Height is highly correlated with Doors and 1 other fieldsHigh correlation
Length is highly correlated with Ex-Showroom_Price and 6 other fieldsHigh correlation
Width is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Doors is highly correlated with HeightHigh correlation
Gears is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Seating_Capacity is highly correlated with HeightHigh correlation
Boot_Space is highly correlated with LengthHigh correlation
Diesel is highly correlated with PetrolHigh correlation
Petrol is highly correlated with DieselHigh correlation
Ex-Showroom_Price is highly correlated with Displacement and 2 other fieldsHigh correlation
Displacement is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Cylinders is highly correlated with Ex-Showroom_Price and 6 other fieldsHigh correlation
Fuel_Tank_Capacity is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Height is highly correlated with Doors and 1 other fieldsHigh correlation
Length is highly correlated with Displacement and 4 other fieldsHigh correlation
Width is highly correlated with Displacement and 4 other fieldsHigh correlation
Doors is highly correlated with Cylinders and 2 other fieldsHigh correlation
Gears is highly correlated with Displacement and 4 other fieldsHigh correlation
Seating_Capacity is highly correlated with Height and 1 other fieldsHigh correlation
Diesel is highly correlated with PetrolHigh correlation
Petrol is highly correlated with DieselHigh correlation
Ex-Showroom_Price is highly correlated with Displacement and 5 other fieldsHigh correlation
Displacement is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Cylinders is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Fuel_Tank_Capacity is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Height is highly correlated with Doors and 1 other fieldsHigh correlation
Length is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Width is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Doors is highly correlated with HeightHigh correlation
Gears is highly correlated with Ex-Showroom_Price and 5 other fieldsHigh correlation
Seating_Capacity is highly correlated with HeightHigh correlation
Diesel is highly correlated with PetrolHigh correlation
Petrol is highly correlated with DieselHigh correlation
Diesel is highly correlated with PetrolHigh correlation
Petrol is highly correlated with DieselHigh correlation
Ex-Showroom_Price is highly correlated with Displacement and 4 other fieldsHigh correlation
Displacement is highly correlated with Ex-Showroom_Price and 9 other fieldsHigh correlation
Cylinders is highly correlated with Ex-Showroom_Price and 7 other fieldsHigh correlation
Fuel_Tank_Capacity is highly correlated with Ex-Showroom_Price and 6 other fieldsHigh correlation
Height is highly correlated with Ex-Showroom_Price and 6 other fieldsHigh correlation
Length is highly correlated with Ex-Showroom_Price and 6 other fieldsHigh correlation
Width is highly correlated with Displacement and 4 other fieldsHigh correlation
Doors is highly correlated with Displacement and 4 other fieldsHigh correlation
Gears is highly correlated with Displacement and 5 other fieldsHigh correlation
Seating_Capacity is highly correlated with Displacement and 4 other fieldsHigh correlation
Basic_Warranty is highly correlated with Displacement and 2 other fieldsHigh correlation
Boot_Space is highly correlated with Fuel_Tank_Capacity and 2 other fieldsHigh correlation
Diesel is highly correlated with PetrolHigh correlation
Petrol is highly correlated with DieselHigh correlation
Electric is highly correlated with Basic_WarrantyHigh correlation
Cylinders has 65 (5.1%) missing values Missing
Valves_Per_Cylinder has 101 (7.9%) missing values Missing
Fuel_Tank_Capacity has 67 (5.3%) missing values Missing
City_Mileage has 1273 (99.9%) missing values Missing
Gears has 105 (8.2%) missing values Missing
Basic_Warranty has 438 (34.4%) missing values Missing
Boot_Space has 247 (19.4%) missing values Missing

Reproduction

Analysis started2022-05-27 06:08:43.908414
Analysis finished2022-05-27 06:09:31.423813
Duration47.52 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Ex-Showroom_Price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1177
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4435365.062
Minimum236447
Maximum192142937
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:31.588026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum236447
5-th percentile467468.8
Q1743628
median1059871
Q32973250
95-th percentile21463405.8
Maximum192142937
Range191906490
Interquartile range (IQR)2229622

Descriptive statistics

Standard deviation10673243.47
Coefficient of variation (CV)2.406395714
Kurtosis88.82592216
Mean4435365.062
Median Absolute Deviation (MAD)468772
Skewness7.230684434
Sum5650655089
Variance1.139181262 × 1014
MonotonicityNot monotonic
2022-05-27T11:39:31.920217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99990014
 
1.1%
9999904
 
0.3%
9890003
 
0.2%
9250003
 
0.2%
7450003
 
0.2%
38300002
 
0.2%
32330002
 
0.2%
34200002
 
0.2%
520000002
 
0.2%
64400002
 
0.2%
Other values (1167)1237
97.1%
ValueCountFrequency (%)
2364471
0.1%
2630001
0.1%
2722231
0.1%
2796501
0.1%
2827781
0.1%
2830001
0.1%
2832901
0.1%
2844851
0.1%
2926671
0.1%
2948001
0.1%
ValueCountFrequency (%)
1921429371
0.1%
950000001
0.1%
837553831
0.1%
773126611
0.1%
754000001
0.1%
695000001
0.1%
592161931
0.1%
532472011
0.1%
532103271
0.1%
525712941
0.1%

Displacement
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct129
Distinct (%)10.2%
Missing11
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean1853.947743
Minimum72
Maximum7993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:32.180065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile998
Q11198
median1497
Q31998
95-th percentile3996
Maximum7993
Range7921
Interquartile range (IQR)800

Descriptive statistics

Standard deviation1049.451565
Coefficient of variation (CV)0.5660631852
Kurtosis7.390867627
Mean1853.947743
Median Absolute Deviation (MAD)301
Skewness2.508613718
Sum2341536
Variance1101348.587
MonotonicityNot monotonic
2022-05-27T11:39:32.434112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1197108
 
8.5%
149887
 
6.8%
124862
 
4.9%
119858
 
4.6%
149756
 
4.4%
99853
 
4.2%
217946
 
3.6%
119939
 
3.1%
146138
 
3.0%
196829
 
2.3%
Other values (119)687
53.9%
ValueCountFrequency (%)
723
 
0.2%
2162
 
0.2%
6246
 
0.5%
79612
 
0.9%
7998
 
0.6%
99853
4.2%
99920
 
1.6%
10471
 
0.1%
10868
 
0.6%
11205
 
0.4%
ValueCountFrequency (%)
79931
 
0.1%
67521
 
0.1%
67501
 
0.1%
67494
0.3%
65981
 
0.1%
65931
 
0.1%
65923
0.2%
64983
0.2%
64961
 
0.1%
64171
 
0.1%

Cylinders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9
Distinct (%)0.7%
Missing65
Missing (%)5.1%
Infinite0
Infinite (%)0.0%
Mean4.371381307
Minimum2
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:32.647162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median4
Q34
95-th percentile8
Maximum16
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.627640441
Coefficient of variation (CV)0.3723400744
Kurtosis10.07180002
Mean4.371381307
Median Absolute Deviation (MAD)0
Skewness2.884777547
Sum5285
Variance2.649213405
MonotonicityNot monotonic
2022-05-27T11:39:32.879159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4789
61.9%
3213
 
16.7%
6106
 
8.3%
853
 
4.2%
1221
 
1.6%
510
 
0.8%
1010
 
0.8%
26
 
0.5%
161
 
0.1%
(Missing)65
 
5.1%
ValueCountFrequency (%)
26
 
0.5%
3213
 
16.7%
4789
61.9%
510
 
0.8%
6106
 
8.3%
853
 
4.2%
1010
 
0.8%
1221
 
1.6%
161
 
0.1%
ValueCountFrequency (%)
161
 
0.1%
1221
 
1.6%
1010
 
0.8%
853
 
4.2%
6106
 
8.3%
510
 
0.8%
4789
61.9%
3213
 
16.7%
26
 
0.5%

Valves_Per_Cylinder
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)0.7%
Missing101
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean3.977834612
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:33.101413image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14
median4
Q34
95-th percentile4
Maximum16
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8341187027
Coefficient of variation (CV)0.2096916499
Kurtosis103.7751373
Mean3.977834612
Median Absolute Deviation (MAD)0
Skewness7.950267485
Sum4666
Variance0.6957540102
MonotonicityNot monotonic
2022-05-27T11:39:33.307333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
41112
87.3%
246
 
3.6%
124
 
0.3%
84
 
0.3%
162
 
0.2%
12
 
0.2%
32
 
0.2%
61
 
0.1%
(Missing)101
 
7.9%
ValueCountFrequency (%)
12
 
0.2%
246
 
3.6%
32
 
0.2%
41112
87.3%
61
 
0.1%
84
 
0.3%
124
 
0.3%
162
 
0.2%
ValueCountFrequency (%)
162
 
0.2%
124
 
0.3%
84
 
0.3%
61
 
0.1%
41112
87.3%
32
 
0.2%
246
 
3.6%
12
 
0.2%

Fuel_Tank_Capacity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct63
Distinct (%)5.2%
Missing67
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean52.11922121
Minimum15
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:33.583270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile35
Q140
median46
Q360
95-th percentile86
Maximum105
Range90
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.45895579
Coefficient of variation (CV)0.3157943539
Kurtosis0.316968109
Mean52.11922121
Median Absolute Deviation (MAD)9
Skewness0.9371756109
Sum62907.9
Variance270.8972256
MonotonicityNot monotonic
2022-05-27T11:39:33.867638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45125
 
9.8%
35124
 
9.7%
5094
 
7.4%
6091
 
7.1%
3779
 
6.2%
5573
 
5.7%
4056
 
4.4%
4251
 
4.0%
7048
 
3.8%
4448
 
3.8%
Other values (53)418
32.8%
(Missing)67
 
5.3%
ValueCountFrequency (%)
151
 
0.1%
245
 
0.4%
2710
 
0.8%
2818
 
1.4%
3221
 
1.6%
35124
9.7%
361
 
0.1%
3779
6.2%
4056
4.4%
4114
 
1.1%
ValueCountFrequency (%)
1056
 
0.5%
1007
 
0.5%
961
 
0.1%
93.54
 
0.3%
937
 
0.5%
922
 
0.2%
912
 
0.2%
90.51
 
0.1%
9021
1.6%
894
 
0.3%

Height
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct211
Distinct (%)16.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1591.408362
Minimum1.845
Maximum2670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:34.119277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.845
5-th percentile1415.6
Q11495
median1557
Q31666
95-th percentile1870
Maximum2670
Range2668.155
Interquartile range (IQR)171

Descriptive statistics

Standard deviation157.6810887
Coefficient of variation (CV)0.09908273228
Kurtosis9.667344133
Mean1591.408362
Median Absolute Deviation (MAD)83
Skewness-0.2381918633
Sum2025862.845
Variance24863.32573
MonotonicityNot monotonic
2022-05-27T11:39:34.348017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152049
 
3.8%
151035
 
2.7%
164033
 
2.6%
156029
 
2.3%
152527
 
2.1%
160727
 
2.1%
149524
 
1.9%
165522
 
1.7%
147522
 
1.7%
178521
 
1.6%
Other values (201)984
77.2%
ValueCountFrequency (%)
1.8451
 
0.1%
11363
 
0.2%
11659
0.7%
12001
 
0.1%
12031
 
0.1%
12111
 
0.1%
12121
 
0.1%
12132
 
0.2%
12501
 
0.1%
12521
 
0.1%
ValueCountFrequency (%)
26701
 
0.1%
20752
 
0.2%
20554
0.3%
19956
0.5%
19772
 
0.2%
19691
 
0.1%
19381
 
0.1%
19304
0.3%
19224
0.3%
19103
0.2%

Length
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct227
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4296.781507
Minimum4.64
Maximum6092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:34.602541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4.64
5-th percentile3606.5
Q13991.25
median4331
Q34620
95-th percentile5115
Maximum6092
Range6087.36
Interquartile range (IQR)628.75

Descriptive statistics

Standard deviation476.9256823
Coefficient of variation (CV)0.1109960284
Kurtosis4.636160941
Mean4296.781507
Median Absolute Deviation (MAD)336
Skewness-0.3357446246
Sum5474099.64
Variance227458.1065
MonotonicityNot monotonic
2022-05-27T11:39:35.245379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3995174
 
13.7%
444036
 
2.8%
458529
 
2.3%
370029
 
2.3%
399427
 
2.1%
439527
 
2.1%
431525
 
2.0%
360020
 
1.6%
427018
 
1.4%
441318
 
1.4%
Other values (217)871
68.4%
ValueCountFrequency (%)
4.641
 
0.1%
27522
 
0.2%
31646
0.5%
33702
 
0.2%
33902
 
0.2%
33951
 
0.1%
34296
0.5%
34301
 
0.1%
34458
0.6%
35455
0.4%
ValueCountFrequency (%)
60921
0.1%
58422
0.2%
56121
0.1%
55751
0.1%
55691
0.1%
54581
0.1%
54531
0.1%
53991
0.1%
53701
0.1%
53411
0.1%

Width
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct182
Distinct (%)14.4%
Missing12
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean1788.343776
Minimum1.845
Maximum2226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:35.481072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.845
5-th percentile1579.05
Q11695
median1770
Q31852
95-th percentile2091
Maximum2226
Range2224.155
Interquartile range (IQR)157

Descriptive statistics

Standard deviation150.8881151
Coefficient of variation (CV)0.08437310382
Kurtosis16.16251079
Mean1788.343776
Median Absolute Deviation (MAD)75
Skewness-0.6546219165
Sum2256889.845
Variance22767.22327
MonotonicityNot monotonic
2022-05-27T11:39:35.756406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169576
 
6.0%
173570
 
5.5%
173036
 
2.8%
181130
 
2.4%
174529
 
2.3%
166028
 
2.2%
169928
 
2.2%
185025
 
2.0%
189024
 
1.9%
181823
 
1.8%
Other values (172)893
70.1%
ValueCountFrequency (%)
1.8451
 
0.1%
13122
 
0.2%
14102
 
0.2%
14591
 
0.1%
14756
 
0.5%
149015
1.2%
152010
0.8%
15402
 
0.2%
15606
 
0.5%
15706
 
0.5%
ValueCountFrequency (%)
22261
 
0.1%
222014
1.1%
22183
 
0.2%
22081
 
0.1%
22072
 
0.2%
22008
0.6%
21942
 
0.2%
21811
 
0.1%
21752
 
0.2%
21694
 
0.3%

Doors
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.3%
Missing4
Missing (%)0.3%
Memory size10.1 KiB
5.0
835 
4.0
362 
2.0
 
61
3.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3810
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row4.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0835
65.5%
4.0362
28.4%
2.061
 
4.8%
3.012
 
0.9%
(Missing)4
 
0.3%

Length

2022-05-27T11:39:35.949901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-27T11:39:36.228723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
5.0835
65.7%
4.0362
28.5%
2.061
 
4.8%
3.012
 
0.9%

Most occurring characters

ValueCountFrequency (%)
.1270
33.3%
01270
33.3%
5835
21.9%
4362
 
9.5%
261
 
1.6%
312
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2540
66.7%
Other Punctuation1270
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01270
50.0%
5835
32.9%
4362
 
14.3%
261
 
2.4%
312
 
0.5%
Other Punctuation
ValueCountFrequency (%)
.1270
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.1270
33.3%
01270
33.3%
5835
21.9%
4362
 
9.5%
261
 
1.6%
312
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.1270
33.3%
01270
33.3%
5835
21.9%
4362
 
9.5%
261
 
1.6%
312
 
0.3%

City_Mileage
Categorical

CONSTANT
MISSING
REJECTED

Distinct1
Distinct (%)100.0%
Missing1273
Missing (%)99.9%
Memory size10.1 KiB
12.7

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row12.7

Common Values

ValueCountFrequency (%)
12.71
 
0.1%
(Missing)1273
99.9%

Length

2022-05-27T11:39:36.449575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-27T11:39:36.645205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
12.71
100.0%

Most occurring characters

ValueCountFrequency (%)
11
25.0%
21
25.0%
.1
25.0%
71
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3
75.0%
Other Punctuation1
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11
33.3%
21
33.3%
71
33.3%
Other Punctuation
ValueCountFrequency (%)
.1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11
25.0%
21
25.0%
.1
25.0%
71
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11
25.0%
21
25.0%
.1
25.0%
71
25.0%

Gears
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.5%
Missing105
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean5.879384089
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:36.775611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5
Q15
median5
Q37
95-th percentile8
Maximum9
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1791544
Coefficient of variation (CV)0.2005574704
Kurtosis-0.1414491445
Mean5.879384089
Median Absolute Deviation (MAD)0
Skewness0.9973986314
Sum6873
Variance1.3904051
MonotonicityNot monotonic
2022-05-27T11:39:36.936067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5614
48.2%
6233
 
18.3%
8139
 
10.9%
7137
 
10.8%
930
 
2.4%
416
 
1.3%
(Missing)105
 
8.2%
ValueCountFrequency (%)
416
 
1.3%
5614
48.2%
6233
 
18.3%
7137
 
10.8%
8139
 
10.9%
930
 
2.4%
ValueCountFrequency (%)
930
 
2.4%
8139
 
10.9%
7137
 
10.8%
6233
 
18.3%
5614
48.2%
416
 
1.3%

Seating_Capacity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.6%
Missing6
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean5.273659306
Minimum2
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:37.153557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median5
Q35
95-th percentile7
Maximum16
Range14
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.141884034
Coefficient of variation (CV)0.2165259392
Kurtosis8.198445515
Mean5.273659306
Median Absolute Deviation (MAD)0
Skewness1.071815555
Sum6687
Variance1.303899148
MonotonicityNot monotonic
2022-05-27T11:39:37.304531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5915
71.8%
7183
 
14.4%
469
 
5.4%
238
 
3.0%
626
 
2.0%
919
 
1.5%
817
 
1.3%
161
 
0.1%
(Missing)6
 
0.5%
ValueCountFrequency (%)
238
 
3.0%
469
 
5.4%
5915
71.8%
626
 
2.0%
7183
 
14.4%
817
 
1.3%
919
 
1.5%
161
 
0.1%
ValueCountFrequency (%)
161
 
0.1%
919
 
1.5%
817
 
1.3%
7183
 
14.4%
626
 
2.0%
5915
71.8%
469
 
5.4%
238
 
3.0%

Basic_Warranty
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct6
Distinct (%)0.7%
Missing438
Missing (%)34.4%
Infinite0
Infinite (%)0.0%
Mean2.970095694
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:37.477362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q33
95-th percentile3
Maximum24
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.352405945
Coefficient of variation (CV)1.128719843
Kurtosis34.37032129
Mean2.970095694
Median Absolute Deviation (MAD)0
Skewness5.925171691
Sum2483
Variance11.23862562
MonotonicityNot monotonic
2022-05-27T11:39:37.634116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2466
36.6%
3332
26.1%
2420
 
1.6%
412
 
0.9%
13
 
0.2%
83
 
0.2%
(Missing)438
34.4%
ValueCountFrequency (%)
13
 
0.2%
2466
36.6%
3332
26.1%
412
 
0.9%
83
 
0.2%
2420
 
1.6%
ValueCountFrequency (%)
2420
 
1.6%
83
 
0.2%
412
 
0.9%
3332
26.1%
2466
36.6%
13
 
0.2%

Boot_Space
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct137
Distinct (%)13.3%
Missing247
Missing (%)19.4%
Infinite0
Infinite (%)0.0%
Mean387.5856865
Minimum20
Maximum1761
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.1 KiB
2022-05-27T11:39:37.850768image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile150
Q1260
median375
Q3475
95-th percentile615.7
Maximum1761
Range1741
Interquartile range (IQR)215

Descriptive statistics

Standard deviation179.1006773
Coefficient of variation (CV)0.4620931152
Kurtosis14.9780265
Mean387.5856865
Median Absolute Deviation (MAD)103
Skewness2.554735182
Sum398050.5
Variance32077.05259
MonotonicityNot monotonic
2022-05-27T11:39:38.121572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35044
 
3.5%
47535
 
2.7%
46031
 
2.4%
51030
 
2.4%
25128
 
2.2%
42024
 
1.9%
25622
 
1.7%
24322
 
1.7%
40021
 
1.6%
23521
 
1.6%
Other values (127)749
58.8%
(Missing)247
 
19.4%
ValueCountFrequency (%)
202
 
0.2%
541
 
0.1%
708
0.6%
942
 
0.2%
9619
1.5%
1107
 
0.5%
1287
 
0.5%
1322
 
0.2%
1331
 
0.1%
1352
 
0.2%
ValueCountFrequency (%)
17612
 
0.2%
17022
 
0.2%
14003
0.2%
10501
 
0.1%
10254
0.3%
9814
0.3%
9096
0.5%
8252
 
0.2%
7701
 
0.1%
7591
 
0.1%

Diesel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
692 
1
582 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1274
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0692
54.3%
1582
45.7%

Length

2022-05-27T11:39:38.349481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-27T11:39:38.530335image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0692
54.3%
1582
45.7%

Most occurring characters

ValueCountFrequency (%)
0692
54.3%
1582
45.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0692
54.3%
1582
45.7%

Most occurring scripts

ValueCountFrequency (%)
Common1274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0692
54.3%
1582
45.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0692
54.3%
1582
45.7%

CNG
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
1252 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1274
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01252
98.3%
122
 
1.7%

Length

2022-05-27T11:39:38.671849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-27T11:39:38.857600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
01252
98.3%
122
 
1.7%

Most occurring characters

ValueCountFrequency (%)
01252
98.3%
122
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01252
98.3%
122
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Common1274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01252
98.3%
122
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01252
98.3%
122
 
1.7%

Petrol
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
1
648 
0
626 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1274
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1648
50.9%
0626
49.1%

Length

2022-05-27T11:39:39.014546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-27T11:39:39.202983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1648
50.9%
0626
49.1%

Most occurring characters

ValueCountFrequency (%)
1648
50.9%
0626
49.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1648
50.9%
0626
49.1%

Most occurring scripts

ValueCountFrequency (%)
Common1274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1648
50.9%
0626
49.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1648
50.9%
0626
49.1%

Electric
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.1 KiB
0
1261 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1274
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01261
99.0%
113
 
1.0%

Length

2022-05-27T11:39:39.387535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-27T11:39:39.576440image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
01261
99.0%
113
 
1.0%

Most occurring characters

ValueCountFrequency (%)
01261
99.0%
113
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1274
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01261
99.0%
113
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common1274
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01261
99.0%
113
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1274
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01261
99.0%
113
 
1.0%

Interactions

2022-05-27T11:39:25.838611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:51.615256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:55.303654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:58.718291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:02.006248image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:04.806197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:08.011254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:10.756928image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:13.658763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:16.905666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:19.563266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:23.005253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:26.061206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:52.109095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:55.790701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:58.996631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:02.290889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:05.049858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:08.257518image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:10.983316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:13.929094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:17.130096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:19.813285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:23.211956image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:26.346184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:52.391285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:56.024870image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:59.263306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:02.523381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:05.282058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:08.534441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:11.253672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:14.547063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:17.344450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:20.109187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:23.448384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:26.584315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:52.589377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:56.261334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:59.546412image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:02.731425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:05.481565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:08.757983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:11.476867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:14.812728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:17.571445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:20.360023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:23.734429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:26.822850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:52.863081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:56.561478image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:00.191027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:02.964810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:05.744253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:08.973411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:11.722366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:15.052099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:17.815120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:20.685071image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:23.943181image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:27.058482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:53.094926image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:56.859762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:00.401984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:03.184826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:05.975400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:09.223003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:11.953936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:15.275470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:18.017906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:20.915886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:24.169621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:27.305876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:53.335640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:57.095687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:00.622790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:03.392492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:06.169682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:09.430788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:12.186356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:15.490873image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:18.221441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:21.192914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:24.411075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:27.565499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:53.598382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:57.361795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:00.842880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:03.605002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:06.408897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:09.671546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:12.413541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:15.705502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:18.425992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:21.793380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:24.633762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:27.785319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:53.819950image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:57.629506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:01.050065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:03.802891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:06.633202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:09.857921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:12.617112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:15.912190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:18.644247image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:22.023253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:24.888359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:28.016663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:54.085351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:57.912596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:01.273494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:04.040740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:07.289068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:10.075917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:12.890599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:16.157858image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:18.838445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:22.270979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:25.128165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:28.247438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:54.406763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:58.210741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:01.513337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:04.288270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:07.525693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:10.315603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:13.147004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:16.411068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:19.060459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:22.503860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:25.383710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:28.491381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:54.898135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:38:58.438557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:01.771672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:04.523562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:07.765689image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:10.532205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:13.416669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:16.688349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:19.309118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:22.734760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-27T11:39:25.628167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-27T11:39:39.759055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-27T11:39:40.601527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-27T11:39:41.092319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-27T11:39:41.538773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-27T11:39:41.824381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-27T11:39:29.269547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-27T11:39:29.943172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-27T11:39:30.560995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-27T11:39:31.120744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Ex-Showroom_PriceDisplacementCylindersValves_Per_CylinderFuel_Tank_CapacityHeightLengthWidthDoorsCity_MileageGearsSeating_CapacityBasic_WarrantyBoot_SpaceDieselCNGPetrolElectric
0292667624.02.02.024.01652.03164.01750.05.0NaN4.04.02.0110.00010
1236447624.02.02.024.01652.03164.01750.05.0NaN4.04.02.0110.00010
2296661624.02.02.015.01652.03164.01750.04.0NaN4.04.02.0110.00100
3334768624.02.02.024.01652.03164.01750.05.0NaN5.04.02.094.00010
4272223624.02.02.024.01652.03164.01750.05.0NaN4.04.02.0110.00010
5314815624.02.02.024.01652.03164.01750.05.0NaN5.04.02.094.00010
6279650799.03.04.028.01541.03429.01560.05.0NaN5.05.02.0222.00010
7351832799.03.04.028.01541.03429.01560.05.0NaN5.05.02.0222.00010
8333419799.03.04.028.01541.03429.01560.05.0NaN5.05.02.0222.00010
9362000799.03.04.028.01541.03429.01560.05.0NaN5.05.02.0222.00010

Last rows

Ex-Showroom_PriceDisplacementCylindersValves_Per_CylinderFuel_Tank_CapacityHeightLengthWidthDoorsCity_MileageGearsSeating_CapacityBasic_WarrantyBoot_SpaceDieselCNGPetrolElectric
126410659001497.04.04.040.01495.04440.01695.04.0NaN5.05.02.0510.00010
126511820001497.04.04.040.01495.04440.01695.04.0NaN5.05.02.0510.00010
126613120001497.04.04.040.01495.04440.01695.04.0NaN5.05.02.0510.00010
126711110001498.04.04.040.01495.04440.01695.04.0NaN6.05.02.0510.01000
126811910001498.04.04.040.01495.04440.01695.04.0NaN6.05.02.0510.01000
126913020001498.04.04.040.01495.04440.01695.04.0NaN6.05.02.0510.01000
127014210001498.04.04.040.01495.04440.01695.04.0NaN6.05.02.0510.01000
127114310001497.04.04.040.01495.04440.01695.04.0NaN5.05.02.0510.00010
127212010001497.04.04.040.01495.04440.01695.04.0NaN5.05.02.0510.00010
127368625603200.04.04.088.01900.04900.01875.05.0NaN5.07.0NaN1050.01000